Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches

This study aims to assess the limitations of the height loss ratio (HLR) method and introduce a new approach that integrates a deep learning (DL) model to enhance vertebral compression fracture (VCF) detection performance. We conducted a retrospective study on 589 patients with chronic VCFs. We comp...

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Bibliographic Details
Published in:Diagnostics (Basel) 2024-11, Vol.14 (22), p.2477
Main Authors: Lee, Jemyoung, Park, Heejun, Yang, Zepa, Woo, Ok Hee, Kang, Woo Young, Kim, Jong Hyo
Format: Article
Language:English
Subjects:
Bone density
Classification
Comparative analysis
Computational linguistics
computed tomography
Datasets
Deep learning
Fractures
height loss ratio
Language processing
Medical imaging equipment
Medical research
Medicine, Experimental
Methods
Mortality
Natural language interfaces
Osteoporosis
Patients
spine
Vertebrae
vertebral compression fracture
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Summary:This study aims to assess the limitations of the height loss ratio (HLR) method and introduce a new approach that integrates a deep learning (DL) model to enhance vertebral compression fracture (VCF) detection performance. We conducted a retrospective study on 589 patients with chronic VCFs. We compared four different methods: HLR-only, DL-only, a combination of HLR and DL for positive VCF, and a combination of HLR and DL for negative VCF. The models were evaluated using dice similarity coefficient, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The combined method (HLR + DL, positive) demonstrated the best performance with an AUROC of 0.968, sensitivity (94.95%), and specificity (90.59%). The HLR-only and the HLR + DL (negative) also showed strong discriminatory power, with AUROCs of 0.948 and 0.947, respectively. The DL-only model achieved the highest specificity (95.92%) but exhibited lower sensitivity (82.83%). Our study highlights the limitations of the HLR method in detecting chronic VCFs and demonstrates the improved performance of combining HLR with DL models.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14222477